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Question 14: Does A Method Help You If The 'known' Is A Lie?

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Posted by Harvey on December 18, 2004 04:37:57 UTC

You seem to be missing the whole issue here. I have defined "an explanation" and, from that definition, constructed a general model applicable to any explanation. In doing that, I discover that all explanations have a curious commonality: they can be interpreted (or mapped to) to a system where my fundamental equation is valid. To me (apparently to me alone) that is an interesting and fundamental discovery.

Before we discuss my views, if you don't mind, I recommend that we don't give up on this approach that I understand your model first. If I don't understand what you're saying, then I can't put my view in the language that you are most familiar.

If "AN explanation" is defined "to be a method of obtaining expectations from given known information" AND the "information contained in that part of 'A' which is given" is a lie, then how can your model "yield a specifc set of expectations for any proposed element of 'A'"?

That is,

Question 14: If the given 'known information' is a lie, then why will a consistent method produce the same results as a situation in which the 'known information' is not a lie? For example, if someone used arithmetic to count sheep, and that method gave the wrong number of sheep, why would that method be valuable? It is wrong, and it is useless, is it not? Is not the golden rule of data processing true: "garbage in/garbage out"?

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